TY  - BOOK
AU  - Hu,Zhengbing
AU  - Bodyanskiy,Yevgeniy V.
AU  - Tyshchenko,Oleksii
TI  - Self-learning and adaptive algorithms for business applications: a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions 
T2  - Emerald points
SN  - 9781838671716
AV  - HD30.2 
U1  - 658.4038 23
PY  - 2019///
CY  - Bingley, UK
PB  - Emerald Publishing
KW  - Electronic data processing
KW  - Business
KW  - Data processing
KW  - Fuzzy systems
KW  - Gestion
KW  - Informatique
KW  - Systèmes flous
KW  - Neural networks & fuzzy systems
KW  - bicssc
KW  - BUSINESS & ECONOMICS
KW  - Industrial Management
KW  - bisacsh
KW  - Management
KW  - Management Science
KW  - Organizational Behavior
KW  - fast
KW  - Electronic books
N1  - Includes bibliographical references; Front Cover; Self-Learning and Adaptive Algorithms for Business Applications; Copyright Page; Contents; Acknowledgment; Introduction; Chapter 1 Review of the Problem Area; 1.1. Learning and Self-learning Procedures; 1.2. Clustering; 1.2.1. Clustering Methods; 1.3. Fuzzy Sets and Fuzzy Logic; 1.3.1. Fuzzy Inference Systems and Fuzzy Control; 1.3.2. Type-2 Fuzzy Logic; 1.3.2.1. Interval Type-2 Fuzzy Sets; 1.3.2.2. Model Reduction; 1.3.2.3. Type-2 Fuzzy Clustering; 1.4. Neural Networks and Their Learning Methods; 1.4.1. Artificial Neural Networks; 1.4.2. Neural Networks' Learning; 1.4.3. Recurrent Neural Networks1.5. Neuro-fuzzy Systems; Chapter 2 Adaptive Methods of Fuzzy Clustering; 2.1. An Objective Function for Fuzzy Clustering; 2.2. Optimization of the Objective Function; 2.3. A Linear Variable Fuzzifier; 2.3.1. Adaptive Fuzzy Clustering with a Variable Fuzzifier; 2.3.2. Possibilistic Fuzzy Clustering with a Variable Fuzzifier; 2.3.3. A Suppression Procedure for Fuzzy Clustering; 2.4. Methods Based on the Gustafson-Kessel Procedure; 2.4.1. The Basic Gustafson-Kessel Method; 2.4.2. A Possibilistic Version of the Gustafson-Kessel Method; 2.4.3. Adaptive Versions of the Gustafson-Kessel Algorithm2.5. A Robust Fuzzy Clustering Method Based on the Cauchy Criterion; 2.5.1. The Probabilistic Approach; 2.5.2. The Possibilistic Approach; Chapter 3 Kohonen Maps and Their Ensembles for Fuzzy Clustering Tasks; 3.1. The Competitive Learning; 3.2. Kohonen Neural Networks; 3.3. Modifications of Kohonen Self-organizing Maps; 3.4. Ensembles and Their Learning Methods; 3.4.1. Reasons for Using Ensembles; 3.4.2. Basic Notions of the Theory of Collective Output Systems; 3.4.2.1. Confidence; 3.4.2.2. Diversification; 3.4.2.3. Incremental Ensembles' Learning3.4.3. Methods for Building Ensembles; 3.4.3.1. An Algebraic Combination; 3.4.3.2. A Weighted Combination; 3.4.3.3. Complex Systems of the Collective Output; 3.5. Ensembles of Neuro-fuzzy Kohonen Networks; 3.6. Fuzzy Type-2 Clustering Using Ensembles of Modified Neuro-fuzzy Kohonen Networks; Chapter 4 Simulation Results and Solutions for Practical Tasks; 4.1. Simulation of the Adaptive Neuro-fuzzy Kohonen Network with a Variable Fuzzifier; 4.1.1. Comparative Efficiency; 4.1.2. The Fuzzifier's Influence; 4.1.3. Influence of the Suppression Parameter; 4.2. Simulation of Adaptive Versions the Gustafson-Kessel Algorithm4.3. Simulation of the Robust Clustering Method Based on the Cauchy Criterion; 4.4. Solving the Task of Automated Cataloging of Illustrative Materials; Conclusion; References
N2  - In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear
UR  - https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2040570
ER  -